Displaying publications 1 - 20 of 116 in total

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  1. Al-Khatib RM, Rashid NA, Abdullah R
    J Biomol Struct Dyn, 2011 Aug;29(1):1-26.
    PMID: 21696223
    The secondary structure of RNA pseudoknots has been extensively inferred and scrutinized by computational approaches. Experimental methods for determining RNA structure are time consuming and tedious; therefore, predictive computational approaches are required. Predicting the most accurate and energy-stable pseudoknot RNA secondary structure has been proven to be an NP-hard problem. In this paper, a new RNA folding approach, termed MSeeker, is presented; it includes KnotSeeker (a heuristic method) and Mfold (a thermodynamic algorithm). The global optimization of this thermodynamic heuristic approach was further enhanced by using a case-based reasoning technique as a local optimization method. MSeeker is a proposed algorithm for predicting RNA pseudoknot structure from individual sequences, especially long ones. This research demonstrates that MSeeker improves the sensitivity and specificity of existing RNA pseudoknot structure predictions. The performance and structural results from this proposed method were evaluated against seven other state-of-the-art pseudoknot prediction methods. The MSeeker method had better sensitivity than the DotKnot, FlexStem, HotKnots, pknotsRG, ILM, NUPACK and pknotsRE methods, with 79% of the predicted pseudoknot base-pairs being correct.
    Matched MeSH terms: Computational Biology/methods*
  2. Hon KW, Ab-Mutalib NS, Abdullah NMA, Jamal R, Abu N
    Sci Rep, 2019 Nov 11;9(1):16497.
    PMID: 31712601 DOI: 10.1038/s41598-019-53063-y
    Chemo-resistance is associated with poor prognosis in colorectal cancer (CRC), with the absence of early biomarker. Exosomes are microvesicles released by body cells for intercellular communication. Circular RNAs (circRNAs) are non-coding RNAs with covalently closed loops and enriched in exosomes. Crosstalk between circRNAs in exosomes and chemo-resistance in CRC remains unknown. This research aims to identify exosomal circRNAs associated with FOLFOX-resistance in CRC. FOLFOX-resistant HCT116 CRC cells (HCT116-R) were generated from parental HCT116 cells (HCT116-P) using periodic drug induction. Exosomes were characterized using transmission electron microscopy (TEM), Zetasizer and Western blot. Our exosomes were translucent cup-shaped structures under TEM with differential expression of TSG101, CD9, and CD63. We performed circRNAs microarray using exosomal RNAs from HCT116-R and HCT116-P cells. We validated our microarray data using serum samples. We performed drug sensitivity assay and cell cycle analysis to characterize selected circRNA after siRNA-knockdown. Using fold change >2 and p 
    Matched MeSH terms: Computational Biology/methods
  3. Chan WH, Mohamad MS, Deris S, Zaki N, Kasim S, Omatu S, et al.
    Comput Biol Med, 2016 10 01;77:102-15.
    PMID: 27522238 DOI: 10.1016/j.compbiomed.2016.08.004
    Incorporation of pathway knowledge into microarray analysis has brought better biological interpretation of the analysis outcome. However, most pathway data are manually curated without specific biological context. Non-informative genes could be included when the pathway data is used for analysis of context specific data like cancer microarray data. Therefore, efficient identification of informative genes is inevitable. Embedded methods like penalized classifiers have been used for microarray analysis due to their embedded gene selection. This paper proposes an improved penalized support vector machine with absolute t-test weighting scheme to identify informative genes and pathways. Experiments are done on four microarray data sets. The results are compared with previous methods using 10-fold cross validation in terms of accuracy, sensitivity, specificity and F-score. Our method shows consistent improvement over the previous methods and biological validation has been done to elucidate the relation of the selected genes and pathway with the phenotype under study.
    Matched MeSH terms: Computational Biology/methods*
  4. Mohamoud HS, Hussain MR, El-Harouni AA, Shaik NA, Qasmi ZU, Merican AF, et al.
    Comput Math Methods Med, 2014;2014:904052.
    PMID: 24723968 DOI: 10.1155/2014/904052
    GalNAc-T1, a key candidate of GalNac-transferases genes family that is involved in mucin-type O-linked glycosylation pathway, is expressed in most biological tissues and cell types. Despite the reported association of GalNAc-T1 gene mutations with human disease susceptibility, the comprehensive computational analysis of coding, noncoding and regulatory SNPs, and their functional impacts on protein level, still remains unknown. Therefore, sequence- and structure-based computational tools were employed to screen the entire listed coding SNPs of GalNAc-T1 gene in order to identify and characterize them. Our concordant in silico analysis by SIFT, PolyPhen-2, PANTHER-cSNP, and SNPeffect tools, identified the potential nsSNPs (S143P, G258V, and Y414D variants) from 18 nsSNPs of GalNAc-T1. Additionally, 2 regulatory SNPs (rs72964406 and #x26; rs34304568) were also identified in GalNAc-T1 by using FastSNP tool. Using multiple computational approaches, we have systematically classified the functional mutations in regulatory and coding regions that can modify expression and function of GalNAc-T1 enzyme. These genetic variants can further assist in better understanding the wide range of disease susceptibility associated with the mucin-based cell signalling and pathogenic binding, and may help to develop novel therapeutic elements for associated diseases.
    Matched MeSH terms: Computational Biology/methods
  5. Ong SY, Ng FL, Badai SS, Yuryev A, Alam M
    J Integr Bioinform, 2010;7(1).
    PMID: 20861532 DOI: 10.2390/biecoll-jib-2010-145
    Signal transduction through protein-protein interactions and protein modifications are the main mechanisms controlling many biological processes. Here we described the implementation of MedScan information extraction technology and Pathway Studio software (Ariadne Genomics Inc.) to create a Salmonella specific molecular interaction database. Using the database, we have constructed several signal transduction pathways in Salmonella enterica serovar Typhi which causes Typhoid Fever, a major health threat especially in developing countries. S. Typhi has several pathogenicity islands that control rapid switching between different phenotypes including adhesion and colonization, invasion, intracellular survival, proliferation, and biofilm formation in response to environmental changes. Understanding of the detailed mechanism for S. Typhi survival in host cells is necessary for development of efficient detection and treatment of this pathogen. The constructed pathways were validated using publically available gene expression microarray data for Salmonella.
    Matched MeSH terms: Computational Biology/methods*
  6. Yeo JG, Wasser M, Kumar P, Pan L, Poh SL, Ally F, et al.
    Nat Biotechnol, 2020 06;38(6):679-684.
    PMID: 32440006 DOI: 10.1038/s41587-020-0532-1
    Matched MeSH terms: Computational Biology/methods*
  7. Firoz A, Malik A, Singh SK, Jha V, Ali A
    Gene, 2015 Dec 15;574(2):235-46.
    PMID: 26260015 DOI: 10.1016/j.gene.2015.08.012
    Glycogenes regulate a large number of biological processes such as cancer and development. In this work, we created an interaction network of 923 glycogenes to detect potential hubs from different mouse tissues using RNA-Seq data. DAVID functional cluster analysis revealed enrichment of immune response, glycoprotein and cholesterol metabolic processes. We also explored nsSNPs that may modify the expression and function of identified hubs using computational methods. We observe that the number of nsSNPs predicted by any two methods to affect protein function is 4, 7 and 2 for FLT1, NID2 and TNFRSF1B. Residues in the native and mutant proteins were analyzed for solvent accessibility and secondary structure change. Analysis of hubs can help in determining their degree of conservation and understanding their functions in biological processes. The nsSNPs proposed in this work may be further targeted through experimental methods for understanding structural and functional relationships of hub mutants.
    Matched MeSH terms: Computational Biology/methods
  8. Forid MS, Rahman MA, Aluwi MFFM, Uddin MN, Roy TG, Mohanta MC, et al.
    Molecules, 2021 Jul 30;26(15).
    PMID: 34361788 DOI: 10.3390/molecules26154634
    This research investigated a UPLC-QTOF/ESI-MS-based phytochemical profiling of Combretum indicum leaf extract (CILEx), and explored its in vitro antioxidant and in vivo antidiabetic effects in a Long-Evans rat model. After a one-week intervention, the animals' blood glucose, lipid profile, and pancreatic architectures were evaluated. UPLC-QTOF/ESI-MS fragmentation of CILEx and its eight docking-guided compounds were further dissected to evaluate their roles using bioinformatics-based network pharmacological tools. Results showed a very promising antioxidative effect of CILEx. Both doses of CILEx were found to significantly (p < 0.05) reduce blood glucose, low-density lipoprotein (LDL), and total cholesterol (TC), and increase high-density lipoprotein (HDL). Pancreatic tissue architectures were much improved compared to the diabetic control group. A computational approach revealed that schizonepetoside E, melianol, leucodelphinidin, and arbutin were highly suitable for further therapeutic assessment. Arbutin, in a Gene Ontology and PPI network study, evolved as the most prospective constituent for 203 target proteins of 48 KEGG pathways regulating immune modulation and insulin secretion to control diabetes. The fragmentation mechanisms of the compounds are consistent with the obtained effects for CILEx. Results show that the natural compounds from CILEx could exert potential antidiabetic effects through in vivo and computational study.
    Matched MeSH terms: Computational Biology/methods
  9. Ramachandran H, Shafie NAH, Sudesh K, Azizan MN, Majid MIA, Amirul AA
    Antonie Van Leeuwenhoek, 2018 Mar;111(3):361-372.
    PMID: 29022146 DOI: 10.1007/s10482-017-0958-8
    Bacterial classification on the basis of a polyphasic approach was conducted on three poly(3 hydroxybutyrate-co-4-hydroxybutyrate) [P(3HB-co-4HB)] accumulating bacterial strains that were isolated from samples collected from Malaysian environments; Kulim Lake, Sg. Pinang river and Sg. Manik paddy field. The Gram-negative, rod-shaped, motile, non-sporulating and non-fermenting bacteria were shown to belong to the genus Cupriavidus of the Betaproteobacteria on the basis of their 16S rRNA gene sequence analyses. The sequence similarity value with their near phylogenetic neighbour, Cupriavidus pauculus LMG3413T, was 98.5%. However, the DNA-DNA hybridization values (8-58%) and ribotyping analysis both enabled these strains to be differentiated from related Cupriavidus species with validly published names. The RiboPrint patterns of the three strains also revealed that the strains were genetically related even though they displayed a clonal diversity. The major cellular fatty acids detected in these strains included C15:0 ISO 2OH/C16:1 ω7c, hexadecanoic (16:0) and cis-11-octadecenoic (C18:1 ω7c). Their G+C contents ranged from 68.0  to 68.6 mol%, and their major isoprenoid quinone was Ubiquinone Q-8. Of these three strains, only strain USMAHM13 (= DSM 25816 = KCTC 32390) was discovered to exhibit yellow pigmentation that is characteristic of the carotenoid family. Their assembled genomes also showed that the three strains were not identical in terms of their genome sizes that were 7.82, 7.95 and 8.70 Mb for strains USMAHM13, USMAA1020 and USMAA2-4, respectively, which are slightly larger than that of Cupriavidus necator H16 (7.42 Mb). The average nucleotide identity (ANI) results indicated that the strains were genetically related and the genome pairs belong to the same species. On the basis of the results obtained in this study, the three strains are considered to represent a novel species for which the name Cupriavidus malaysiensis sp. nov. is proposed. The type strain of the species is USMAA1020T (= DSM 19416T = KCTC 32390T).
    Matched MeSH terms: Computational Biology/methods
  10. Abdullah A, Deris S, Mohamad MS, Anwar S
    PLoS One, 2013;8(4):e61258.
    PMID: 23593445 DOI: 10.1371/journal.pone.0061258
    One of the key aspects of computational systems biology is the investigation on the dynamic biological processes within cells. Computational models are often required to elucidate the mechanisms and principles driving the processes because of the nonlinearity and complexity. The models usually incorporate a set of parameters that signify the physical properties of the actual biological systems. In most cases, these parameters are estimated by fitting the model outputs with the corresponding experimental data. However, this is a challenging task because the available experimental data are frequently noisy and incomplete. In this paper, a new hybrid optimization method is proposed to estimate these parameters from the noisy and incomplete experimental data. The proposed method, called Swarm-based Chemical Reaction Optimization, integrates the evolutionary searching strategy employed by the Chemical Reaction Optimization, into the neighbouring searching strategy of the Firefly Algorithm method. The effectiveness of the method was evaluated using a simulated nonlinear model and two biological models: synthetic transcriptional oscillators, and extracellular protease production models. The results showed that the accuracy and computational speed of the proposed method were better than the existing Differential Evolution, Firefly Algorithm and Chemical Reaction Optimization methods. The reliability of the estimated parameters was statistically validated, which suggests that the model outputs produced by these parameters were valid even when noisy and incomplete experimental data were used. Additionally, Akaike Information Criterion was employed to evaluate the model selection, which highlighted the capability of the proposed method in choosing a plausible model based on the experimental data. In conclusion, this paper presents the effectiveness of the proposed method for parameter estimation and model selection problems using noisy and incomplete experimental data. This study is hoped to provide a new insight in developing more accurate and reliable biological models based on limited and low quality experimental data.
    Matched MeSH terms: Computational Biology/methods*
  11. Pipatchartlearnwong K, Swatdipong A, Vuttipongchaikij S, Apisitwanich S
    BMC Genet, 2017 10 12;18(1):88.
    PMID: 29025415 DOI: 10.1186/s12863-017-0554-y
    BACKGROUND: Borassus flabellifer or Asian Palmyra palm is an important crop for local economies in the South and Southeast Asia for its fruit and palm sugar production. Archeological and historical evidence indicated the presence of this species in Southeast Asia dating back at least 1500 years. B. flabellifer is believed to be originated in Africa, spread to South Asia and introduced into Southeast Asia through commercial routes and dissemination of cultures, however, the nature of its invasion and settlement in Thailand is unclear.

    RESULTS: Here, we analyzed genetic data of 230 B. flabellifer accessions across Thailand using 17 EST-SSR and 12 gSSR polymorphic markers. Clustering analysis revealed that the population consisted of two genetic clusters (STRUCTURE K = 2). Cluster I is found mainly in southern Thailand, while Cluster II is found mainly in the northeastern. Those found in the central are of an extensive mix between the two. These two clusters are in moderate differentiation (F ST = 0.066 and N M = 3.532) and have low genetic diversity (HO = 0.371 and 0.416; AR = 2.99 and 3.19, for the cluster I and II respectively). The minimum numbers of founders for each genetic group varies from 3 to 4 individuals, based on simulation using different allele frequency assumptions. These numbers coincide with that B. flabellifer is dioecious, and a number of seeds had to be simultaneously introduced for obtaining both male and female founders.

    CONCLUSIONS: From these data and geographical and historical evidence, we hypothesize that there were at least two different invasive events of B. flabellifer in Thailand. B. flabellifer was likely brought through the Straits of Malacca to be propagated in the southern Thailand as one of the invasive events before spreading to the central Thailand. The second event likely occurred in Khmer Empire, currently Cambodia, before spreading to the northeastern Thailand.

    Matched MeSH terms: Computational Biology/methods*
  12. Grandjean F, Tan MH, Gan HY, Gan HM, Austin CM
    PMID: 25738217 DOI: 10.3109/19401736.2015.1018207
    The Austropotamobius pallipes complete mitogenome has been recovered using Next-Gen sequencing. Our sample of A. pallipes has a mitogenome of 15,679 base pairs (68.44% A + T content) made up of 13 protein-coding genes, 2 ribosomal subunit genes, 22 transfer RNAs, and a 877 bp non-coding AT-rich region. This is the first mitogenome sequenced for a crayfish from the family Astacidae and the 4(th) for northern hemisphere genera.
    Matched MeSH terms: Computational Biology/methods
  13. Gan HM, Gan HY, Tan MH, Penny SS, Willan RC, Austin CM
    PMID: 25648928 DOI: 10.3109/19401736.2015.1007355
    The complete mitochondrial genome of the commercially and ecologically important and internationally vulnerable giant clam Tridacna squamosa was recovered by genome skimming using the MiSeq platform. The T. squamosa mitogenome has 20,930 base pairs (62.35% A+T content) and is made up of 12 protein-coding genes, 2 ribosomal subunit genes, 24 transfer RNAs, and a 2594 bp non-coding AT-rich region. The mitogenome has a relatively large insertion in the atp6 gene. This is the first mitogenome to be sequenced from the genus Tridacna, and the family Tridacnidae and represents a new gene order.
    Matched MeSH terms: Computational Biology/methods
  14. Gan HM, Grandjean F, Jenkins TL, Austin CM
    BMC Genomics, 2019 May 03;20(1):335.
    PMID: 31053062 DOI: 10.1186/s12864-019-5704-3
    BACKGROUND: The recently published complete mitogenome of the European lobster (Homarus gammarus) that was generated using long-range PCR exhibits unusual gene composition (missing nad2) and gene rearrangements among decapod crustaceans with strong implications in crustacean phylogenetics. Such atypical mitochondrial features will benefit greatly from validation with emerging long read sequencing technologies such as Oxford Nanopore that can more accurately identify structural variation.

    RESULTS: We re-sequenced the H. gammarus mitogenome on an Oxford Nanopore Minion flowcell and performed a long-read only assembly, generating a complete mitogenome assembly for H. gammarus. In contrast to previous reporting, we found an intact mitochondrial nad2 gene in the H. gammarus mitogenome and showed that its gene organization is broadly similar to that of the American lobster (H. americanus) except for the presence of a large tandemly duplicated region with evidence of pseudogenization in one of each duplicated protein-coding genes.

    CONCLUSIONS: Using the European lobster as an example, we demonstrate the value of Oxford Nanopore long read technology in resolving problematic mitogenome assemblies. The increasing accessibility of Oxford Nanopore technology will make it an attractive and useful tool for evolutionary biologists to verify new and existing unusual mitochondrial gene rearrangements recovered using first and second generation sequencing technologies, particularly those used to make phylogenetic inferences of evolutionary scenarios.

    Matched MeSH terms: Computational Biology/methods*
  15. Dzaki N, Ramli KN, Azlan A, Ishak IH, Azzam G
    Sci Rep, 2017 03 16;7:43618.
    PMID: 28300076 DOI: 10.1038/srep43618
    The mosquito Aedes aegypti (Ae. aegypti) is the most notorious vector of illness-causing viruses such as Dengue, Chikugunya, and Zika. Although numerous genetic expression studies utilizing quantitative real-time PCR (qPCR) have been conducted with regards to Ae. aegypti, a panel of genes to be used suitably as references for the purpose of expression-level normalization within this epidemiologically important insect is presently lacking. Here, the usability of seven widely-utilized reference genes i.e. actin (ACT), eukaryotic elongation factor 1 alpha (eEF1α), alpha tubulin (α-tubulin), ribosomal proteins L8, L32 and S17 (RPL8, RPL32 and RPS17), and glyceraldeyde 3-phosphate dehydrogenase (GAPDH) were investigated. Expression patterns of the reference genes were observed in sixteen pre-determined developmental stages and in cell culture. Gene stability was inferred from qPCR data through three freely available algorithms i.e. BestKeeper, geNorm, and NormFinder. The consensus rankings generated from stability values provided by these programs suggest a combination of at least two genes for normalization. ACT and RPS17 are the most dependably expressed reference genes and therefore, we propose an ACT/RPS17 combination for normalization in all Ae. aegypti derived samples. GAPDH performed least desirably, and is thus not a recommended reference gene. This study emphasizes the importance of validating reference genes in Ae. aegypti for qPCR based research.
    Matched MeSH terms: Computational Biology/methods
  16. Tong DL, Kempsell KE, Szakmany T, Ball G
    Front Immunol, 2020;11:380.
    PMID: 32318053 DOI: 10.3389/fimmu.2020.00380
    Sepsis is defined as dysregulated host response caused by systemic infection, leading to organ failure. It is a life-threatening condition, often requiring admission to an intensive care unit (ICU). The causative agents and processes involved are multifactorial but are characterized by an overarching inflammatory response, sharing elements in common with severe inflammatory response syndrome (SIRS) of non-infectious origin. Sepsis presents with a range of pathophysiological and genetic features which make clinical differentiation from SIRS very challenging. This may reflect a poor understanding of the key gene inter-activities and/or pathway associations underlying these disease processes. Improved understanding is critical for early differential recognition of sepsis and SIRS and to improve patient management and clinical outcomes. Judicious selection of gene biomarkers suitable for development of diagnostic tests/testing could make differentiation of sepsis and SIRS feasible. Here we describe a methodologic framework for the identification and validation of biomarkers in SIRS, sepsis and septic shock patients, using a 2-tier gene screening, artificial neural network (ANN) data mining technique, using previously published gene expression datasets. Eight key hub markers have been identified which may delineate distinct, core disease processes and which show potential for informing underlying immunological and pathological processes and thus patient stratification and treatment. These do not show sufficient fold change differences between the different disease states to be useful as primary diagnostic biomarkers, but are instrumental in identifying candidate pathways and other associated biomarkers for further exploration.
    Matched MeSH terms: Computational Biology/methods*
  17. Ishaq M, Khan A, Su'ud MM, Alam MM, Bangash JI, Khan A
    Comput Math Methods Med, 2022;2022:8691646.
    PMID: 35126641 DOI: 10.1155/2022/8691646
    Task scheduling in parallel multiple sequence alignment (MSA) through improved dynamic programming optimization speeds up alignment processing. The increased importance of multiple matching sequences also needs the utilization of parallel processor systems. This dynamic algorithm proposes improved task scheduling in case of parallel MSA. Specifically, the alignment of several tertiary structured proteins is computationally complex than simple word-based MSA. Parallel task processing is computationally more efficient for protein-structured based superposition. The basic condition for the application of dynamic programming is also fulfilled, because the task scheduling problem has multiple possible solutions or options. Search space reduction for speedy processing of this algorithm is carried out through greedy strategy. Performance in terms of better results is ensured through computationally expensive recursive and iterative greedy approaches. Any optimal scheduling schemes show better performance in heterogeneous resources using CPU or GPU.
    Matched MeSH terms: Computational Biology/methods*
  18. Ahmad M, Jung LT, Bhuiyan AA
    Comput Methods Programs Biomed, 2017 Oct;149:11-17.
    PMID: 28802326 DOI: 10.1016/j.cmpb.2017.06.021
    BACKGROUND AND OBJECTIVE: Digital signal processing techniques commonly employ fixed length window filters to process the signal contents. DNA signals differ in characteristics from common digital signals since they carry nucleotides as contents. The nucleotides own genetic code context and fuzzy behaviors due to their special structure and order in DNA strand. Employing conventional fixed length window filters for DNA signal processing produce spectral leakage and hence results in signal noise. A biological context aware adaptive window filter is required to process the DNA signals.

    METHODS: This paper introduces a biological inspired fuzzy adaptive window median filter (FAWMF) which computes the fuzzy membership strength of nucleotides in each slide of window and filters nucleotides based on median filtering with a combination of s-shaped and z-shaped filters. Since coding regions cause 3-base periodicity by an unbalanced nucleotides' distribution producing a relatively high bias for nucleotides' usage, such fundamental characteristic of nucleotides has been exploited in FAWMF to suppress the signal noise.

    RESULTS: Along with adaptive response of FAWMF, a strong correlation between median nucleotides and the Π shaped filter was observed which produced enhanced discrimination between coding and non-coding regions contrary to fixed length conventional window filters. The proposed FAWMF attains a significant enhancement in coding regions identification i.e. 40% to 125% as compared to other conventional window filters tested over more than 250 benchmarked and randomly taken DNA datasets of different organisms.

    CONCLUSION: This study proves that conventional fixed length window filters applied to DNA signals do not achieve significant results since the nucleotides carry genetic code context. The proposed FAWMF algorithm is adaptive and outperforms significantly to process DNA signal contents. The algorithm applied to variety of DNA datasets produced noteworthy discrimination between coding and non-coding regions contrary to fixed window length conventional filters.

    Matched MeSH terms: Computational Biology/methods*
  19. Mirsafian H, Mat Ripen A, Merican AF, Bin Mohamad S
    ScientificWorldJournal, 2014;2014:482463.
    PMID: 25254246 DOI: 10.1155/2014/482463
    Beta-amyloid precursor protein cleavage enzyme 1 (BACE1) and beta-amyloid precursor protein cleavage enzyme 2 (BACE2), members of aspartyl protease family, are close homologues and have high similarity in their protein crystal structures. However, their enzymatic properties differ leading to disparate clinical consequences. In order to identify the residues that are responsible for such differences, we used evolutionary trace (ET) method to compare the amino acid conservation patterns of BACE1 and BACE2 in several mammalian species. We found that, in BACE1 and BACE2 structures, most of the ligand binding sites are conserved which indicate their enzymatic property of aspartyl protease family members. The other conserved residues are more or less randomly localized in other parts of the structures. Four group-specific residues were identified at the ligand binding site of BACE1 and BACE2. We postulated that these residues would be essential for selectivity of BACE1 and BACE2 biological functions and could be sites of interest for the design of selective inhibitors targeting either BACE1 or BACE2.
    Matched MeSH terms: Computational Biology/methods*
  20. Alballa M, Aplop F, Butler G
    PLoS One, 2020;15(1):e0227683.
    PMID: 31935244 DOI: 10.1371/journal.pone.0227683
    Transporters mediate the movement of compounds across the membranes that separate the cell from its environment and across the inner membranes surrounding cellular compartments. It is estimated that one third of a proteome consists of membrane proteins, and many of these are transport proteins. Given the increase in the number of genomes being sequenced, there is a need for computational tools that predict the substrates that are transported by the transmembrane transport proteins. In this paper, we present TranCEP, a predictor of the type of substrate transported by a transmembrane transport protein. TranCEP combines the traditional use of the amino acid composition of the protein, with evolutionary information captured in a multiple sequence alignment (MSA), and restriction to important positions of the alignment that play a role in determining the specificity of the protein. Our experimental results show that TranCEP significantly outperforms the state-of-the-art predictors. The results quantify the contribution made by each type of information used.
    Matched MeSH terms: Computational Biology/methods*
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